// Copyright (c) 2011 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

#include <algorithm>

#include "base/logging.h"
#include "skia/ext/convolver.h"
#include "skia/ext/convolver_SSE2.h"
#include "skia/ext/convolver_mips_dspr2.h"
#include "third_party/skia/include/core/SkSize.h"
#include "third_party/skia/include/core/SkTypes.h"

namespace skia {

namespace {

    // Converts the argument to an 8-bit unsigned value by clamping to the range
    // 0-255.
    inline unsigned char ClampTo8(int a)
    {
        if (static_cast<unsigned>(a) < 256)
            return a; // Avoid the extra check in the common case.
        if (a < 0)
            return 0;
        return 255;
    }

    // Takes the value produced by accumulating element-wise product of image with
    // a kernel and brings it back into range.
    // All of the filter scaling factors are in fixed point with kShiftBits bits of
    // fractional part.
    inline unsigned char BringBackTo8(int a, bool take_absolute)
    {
        a >>= ConvolutionFilter1D::kShiftBits;
        if (take_absolute)
            a = std::abs(a);
        return ClampTo8(a);
    }

    // Stores a list of rows in a circular buffer. The usage is you write into it
    // by calling AdvanceRow. It will keep track of which row in the buffer it
    // should use next, and the total number of rows added.
    class CircularRowBuffer {
    public:
        // The number of pixels in each row is given in |source_row_pixel_width|.
        // The maximum number of rows needed in the buffer is |max_y_filter_size|
        // (we only need to store enough rows for the biggest filter).
        //
        // We use the |first_input_row| to compute the coordinates of all of the
        // following rows returned by Advance().
        CircularRowBuffer(int dest_row_pixel_width, int max_y_filter_size,
            int first_input_row)
            : row_byte_width_(dest_row_pixel_width * 4)
            , num_rows_(max_y_filter_size)
            , next_row_(0)
            , next_row_coordinate_(first_input_row)
        {
            buffer_.resize(row_byte_width_ * max_y_filter_size);
            row_addresses_.resize(num_rows_);
        }

        // Moves to the next row in the buffer, returning a pointer to the beginning
        // of it.
        unsigned char* AdvanceRow()
        {
            unsigned char* row = &buffer_[next_row_ * row_byte_width_];
            next_row_coordinate_++;

            // Set the pointer to the next row to use, wrapping around if necessary.
            next_row_++;
            if (next_row_ == num_rows_)
                next_row_ = 0;
            return row;
        }

        // Returns a pointer to an "unrolled" array of rows. These rows will start
        // at the y coordinate placed into |*first_row_index| and will continue in
        // order for the maximum number of rows in this circular buffer.
        //
        // The |first_row_index_| may be negative. This means the circular buffer
        // starts before the top of the image (it hasn't been filled yet).
        unsigned char* const* GetRowAddresses(int* first_row_index)
        {
            // Example for a 4-element circular buffer holding coords 6-9.
            //   Row 0   Coord 8
            //   Row 1   Coord 9
            //   Row 2   Coord 6  <- next_row_ = 2, next_row_coordinate_ = 10.
            //   Row 3   Coord 7
            //
            // The "next" row is also the first (lowest) coordinate. This computation
            // may yield a negative value, but that's OK, the math will work out
            // since the user of this buffer will compute the offset relative
            // to the first_row_index and the negative rows will never be used.
            *first_row_index = next_row_coordinate_ - num_rows_;

            int cur_row = next_row_;
            for (int i = 0; i < num_rows_; i++) {
                row_addresses_[i] = &buffer_[cur_row * row_byte_width_];

                // Advance to the next row, wrapping if necessary.
                cur_row++;
                if (cur_row == num_rows_)
                    cur_row = 0;
            }
            return &row_addresses_[0];
        }

    private:
        // The buffer storing the rows. They are packed, each one row_byte_width_.
        std::vector<unsigned char> buffer_;

        // Number of bytes per row in the |buffer_|.
        int row_byte_width_;

        // The number of rows available in the buffer.
        int num_rows_;

        // The next row index we should write into. This wraps around as the
        // circular buffer is used.
        int next_row_;

        // The y coordinate of the |next_row_|. This is incremented each time a
        // new row is appended and does not wrap.
        int next_row_coordinate_;

        // Buffer used by GetRowAddresses().
        std::vector<unsigned char*> row_addresses_;
    };

    // Convolves horizontally along a single row. The row data is given in
    // |src_data| and continues for the num_values() of the filter.
    template <bool has_alpha>
    void ConvolveHorizontally(const unsigned char* src_data,
        const ConvolutionFilter1D& filter,
        unsigned char* out_row)
    {
        // Loop over each pixel on this row in the output image.
        int num_values = filter.num_values();
        for (int out_x = 0; out_x < num_values; out_x++) {
            // Get the filter that determines the current output pixel.
            int filter_offset, filter_length;
            const ConvolutionFilter1D::Fixed* filter_values = filter.FilterForValue(out_x, &filter_offset, &filter_length);

            // Compute the first pixel in this row that the filter affects. It will
            // touch |filter_length| pixels (4 bytes each) after this.
            const unsigned char* row_to_filter = &src_data[filter_offset * 4];

            // Apply the filter to the row to get the destination pixel in |accum|.
            int accum[4] = { 0 };
            for (int filter_x = 0; filter_x < filter_length; filter_x++) {
                ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_x];
                accum[0] += cur_filter * row_to_filter[filter_x * 4 + 0];
                accum[1] += cur_filter * row_to_filter[filter_x * 4 + 1];
                accum[2] += cur_filter * row_to_filter[filter_x * 4 + 2];
                if (has_alpha)
                    accum[3] += cur_filter * row_to_filter[filter_x * 4 + 3];
            }

            // Bring this value back in range. All of the filter scaling factors
            // are in fixed point with kShiftBits bits of fractional part.
            accum[0] >>= ConvolutionFilter1D::kShiftBits;
            accum[1] >>= ConvolutionFilter1D::kShiftBits;
            accum[2] >>= ConvolutionFilter1D::kShiftBits;
            if (has_alpha)
                accum[3] >>= ConvolutionFilter1D::kShiftBits;

            // Store the new pixel.
            out_row[out_x * 4 + 0] = ClampTo8(accum[0]);
            out_row[out_x * 4 + 1] = ClampTo8(accum[1]);
            out_row[out_x * 4 + 2] = ClampTo8(accum[2]);
            if (has_alpha)
                out_row[out_x * 4 + 3] = ClampTo8(accum[3]);
        }
    }

    // Does vertical convolution to produce one output row. The filter values and
    // length are given in the first two parameters. These are applied to each
    // of the rows pointed to in the |source_data_rows| array, with each row
    // being |pixel_width| wide.
    //
    // The output must have room for |pixel_width * 4| bytes.
    template <bool has_alpha>
    void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values,
        int filter_length,
        unsigned char* const* source_data_rows,
        int pixel_width,
        unsigned char* out_row)
    {
        // We go through each column in the output and do a vertical convolution,
        // generating one output pixel each time.
        for (int out_x = 0; out_x < pixel_width; out_x++) {
            // Compute the number of bytes over in each row that the current column
            // we're convolving starts at. The pixel will cover the next 4 bytes.
            int byte_offset = out_x * 4;

            // Apply the filter to one column of pixels.
            int accum[4] = { 0 };
            for (int filter_y = 0; filter_y < filter_length; filter_y++) {
                ConvolutionFilter1D::Fixed cur_filter = filter_values[filter_y];
                accum[0] += cur_filter * source_data_rows[filter_y][byte_offset + 0];
                accum[1] += cur_filter * source_data_rows[filter_y][byte_offset + 1];
                accum[2] += cur_filter * source_data_rows[filter_y][byte_offset + 2];
                if (has_alpha)
                    accum[3] += cur_filter * source_data_rows[filter_y][byte_offset + 3];
            }

            // Bring this value back in range. All of the filter scaling factors
            // are in fixed point with kShiftBits bits of precision.
            accum[0] >>= ConvolutionFilter1D::kShiftBits;
            accum[1] >>= ConvolutionFilter1D::kShiftBits;
            accum[2] >>= ConvolutionFilter1D::kShiftBits;
            if (has_alpha)
                accum[3] >>= ConvolutionFilter1D::kShiftBits;

            // Store the new pixel.
            out_row[byte_offset + 0] = ClampTo8(accum[0]);
            out_row[byte_offset + 1] = ClampTo8(accum[1]);
            out_row[byte_offset + 2] = ClampTo8(accum[2]);
            if (has_alpha) {
                unsigned char alpha = ClampTo8(accum[3]);

                // Make sure the alpha channel doesn't come out smaller than any of the
                // color channels. We use premultipled alpha channels, so this should
                // never happen, but rounding errors will cause this from time to time.
                // These "impossible" colors will cause overflows (and hence random pixel
                // values) when the resulting bitmap is drawn to the screen.
                //
                // We only need to do this when generating the final output row (here).
                int max_color_channel = std::max(out_row[byte_offset + 0],
                    std::max(out_row[byte_offset + 1], out_row[byte_offset + 2]));
                if (alpha < max_color_channel)
                    out_row[byte_offset + 3] = max_color_channel;
                else
                    out_row[byte_offset + 3] = alpha;
            } else {
                // No alpha channel, the image is opaque.
                out_row[byte_offset + 3] = 0xff;
            }
        }
    }

    void ConvolveVertically(const ConvolutionFilter1D::Fixed* filter_values,
        int filter_length,
        unsigned char* const* source_data_rows,
        int pixel_width,
        unsigned char* out_row,
        bool source_has_alpha)
    {
        if (source_has_alpha) {
            ConvolveVertically<true>(filter_values, filter_length,
                source_data_rows,
                pixel_width,
                out_row);
        } else {
            ConvolveVertically<false>(filter_values, filter_length,
                source_data_rows,
                pixel_width,
                out_row);
        }
    }

} // namespace

// ConvolutionFilter1D ---------------------------------------------------------

ConvolutionFilter1D::ConvolutionFilter1D()
    : max_filter_(0)
{
}

ConvolutionFilter1D::~ConvolutionFilter1D()
{
}

void ConvolutionFilter1D::AddFilter(int filter_offset,
    const float* filter_values,
    int filter_length)
{
    SkASSERT(filter_length > 0);

    std::vector<Fixed> fixed_values;
    fixed_values.reserve(filter_length);

    for (int i = 0; i < filter_length; ++i)
        fixed_values.push_back(FloatToFixed(filter_values[i]));

    AddFilter(filter_offset, &fixed_values[0], filter_length);
}

void ConvolutionFilter1D::AddFilter(int filter_offset,
    const Fixed* filter_values,
    int filter_length)
{
    // It is common for leading/trailing filter values to be zeros. In such
    // cases it is beneficial to only store the central factors.
    // For a scaling to 1/4th in each dimension using a Lanczos-2 filter on
    // a 1080p image this optimization gives a ~10% speed improvement.
    int filter_size = filter_length;
    int first_non_zero = 0;
    while (first_non_zero < filter_length && filter_values[first_non_zero] == 0)
        first_non_zero++;

    if (first_non_zero < filter_length) {
        // Here we have at least one non-zero factor.
        int last_non_zero = filter_length - 1;
        while (last_non_zero >= 0 && filter_values[last_non_zero] == 0)
            last_non_zero--;

        filter_offset += first_non_zero;
        filter_length = last_non_zero + 1 - first_non_zero;
        SkASSERT(filter_length > 0);

        for (int i = first_non_zero; i <= last_non_zero; i++)
            filter_values_.push_back(filter_values[i]);
    } else {
        // Here all the factors were zeroes.
        filter_length = 0;
    }

    FilterInstance instance;

    // We pushed filter_length elements onto filter_values_
    instance.data_location = (static_cast<int>(filter_values_.size()) - filter_length);
    instance.offset = filter_offset;
    instance.trimmed_length = filter_length;
    instance.length = filter_size;
    filters_.push_back(instance);

    max_filter_ = std::max(max_filter_, filter_length);
}

const ConvolutionFilter1D::Fixed* ConvolutionFilter1D::GetSingleFilter(
    int* specified_filter_length,
    int* filter_offset,
    int* filter_length) const
{
    const FilterInstance& filter = filters_[0];
    *filter_offset = filter.offset;
    *filter_length = filter.trimmed_length;
    *specified_filter_length = filter.length;
    if (filter.trimmed_length == 0)
        return NULL;

    return &filter_values_[filter.data_location];
}

typedef void (*ConvolveVertically_pointer)(
    const ConvolutionFilter1D::Fixed* filter_values,
    int filter_length,
    unsigned char* const* source_data_rows,
    int pixel_width,
    unsigned char* out_row,
    bool has_alpha);
typedef void (*Convolve4RowsHorizontally_pointer)(
    const unsigned char* src_data[4],
    const ConvolutionFilter1D& filter,
    unsigned char* out_row[4]);
typedef void (*ConvolveHorizontally_pointer)(
    const unsigned char* src_data,
    const ConvolutionFilter1D& filter,
    unsigned char* out_row,
    bool has_alpha);

struct ConvolveProcs {
    // This is how many extra pixels may be read by the
    // conolve*horizontally functions.
    int extra_horizontal_reads;
    ConvolveVertically_pointer convolve_vertically;
    Convolve4RowsHorizontally_pointer convolve_4rows_horizontally;
    ConvolveHorizontally_pointer convolve_horizontally;
};

void SetupSIMD(ConvolveProcs* procs)
{
#ifdef SIMD_SSE2
    procs->extra_horizontal_reads = 3;
    procs->convolve_vertically = &ConvolveVertically_SSE2;
    procs->convolve_4rows_horizontally = &Convolve4RowsHorizontally_SSE2;
    procs->convolve_horizontally = &ConvolveHorizontally_SSE2;
#elif defined SIMD_MIPS_DSPR2
    procs->extra_horizontal_reads = 3;
    procs->convolve_vertically = &ConvolveVertically_mips_dspr2;
    procs->convolve_horizontally = &ConvolveHorizontally_mips_dspr2;
#endif
}

void BGRAConvolve2D(const unsigned char* source_data,
    int source_byte_row_stride,
    bool source_has_alpha,
    const ConvolutionFilter1D& filter_x,
    const ConvolutionFilter1D& filter_y,
    int output_byte_row_stride,
    unsigned char* output,
    bool use_simd_if_possible)
{
    ConvolveProcs simd;
    simd.extra_horizontal_reads = 0;
    simd.convolve_vertically = NULL;
    simd.convolve_4rows_horizontally = NULL;
    simd.convolve_horizontally = NULL;
    if (use_simd_if_possible) {
        SetupSIMD(&simd);
    }

    int max_y_filter_size = filter_y.max_filter();

    // The next row in the input that we will generate a horizontally
    // convolved row for. If the filter doesn't start at the beginning of the
    // image (this is the case when we are only resizing a subset), then we
    // don't want to generate any output rows before that. Compute the starting
    // row for convolution as the first pixel for the first vertical filter.
    int filter_offset, filter_length;
    const ConvolutionFilter1D::Fixed* filter_values = filter_y.FilterForValue(0, &filter_offset, &filter_length);
    int next_x_row = filter_offset;

    // We loop over each row in the input doing a horizontal convolution. This
    // will result in a horizontally convolved image. We write the results into
    // a circular buffer of convolved rows and do vertical convolution as rows
    // are available. This prevents us from having to store the entire
    // intermediate image and helps cache coherency.
    // We will need four extra rows to allow horizontal convolution could be done
    // simultaneously. We also padding each row in row buffer to be aligned-up to
    // 16 bytes.
    // TODO(jiesun): We do not use aligned load from row buffer in vertical
    // convolution pass yet. Somehow Windows does not like it.
    int row_buffer_width = (filter_x.num_values() + 15) & ~0xF;
    int row_buffer_height = max_y_filter_size + (simd.convolve_4rows_horizontally ? 4 : 0);
    CircularRowBuffer row_buffer(row_buffer_width,
        row_buffer_height,
        filter_offset);

    // Loop over every possible output row, processing just enough horizontal
    // convolutions to run each subsequent vertical convolution.
    SkASSERT(output_byte_row_stride >= filter_x.num_values() * 4);
    int num_output_rows = filter_y.num_values();

    // We need to check which is the last line to convolve before we advance 4
    // lines in one iteration.
    int last_filter_offset, last_filter_length;

    // SSE2 can access up to 3 extra pixels past the end of the
    // buffer. At the bottom of the image, we have to be careful
    // not to access data past the end of the buffer. Normally
    // we fall back to the C++ implementation for the last row.
    // If the last row is less than 3 pixels wide, we may have to fall
    // back to the C++ version for more rows. Compute how many
    // rows we need to avoid the SSE implementation for here.
    filter_x.FilterForValue(filter_x.num_values() - 1, &last_filter_offset,
        &last_filter_length);
    int avoid_simd_rows = 1 + simd.extra_horizontal_reads / (last_filter_offset + last_filter_length);

    filter_y.FilterForValue(num_output_rows - 1, &last_filter_offset,
        &last_filter_length);

    for (int out_y = 0; out_y < num_output_rows; out_y++) {
        filter_values = filter_y.FilterForValue(out_y,
            &filter_offset, &filter_length);

        // Generate output rows until we have enough to run the current filter.
        while (next_x_row < filter_offset + filter_length) {
            if (simd.convolve_4rows_horizontally && next_x_row + 3 < last_filter_offset + last_filter_length - avoid_simd_rows) {
                const unsigned char* src[4];
                unsigned char* out_row[4];
                for (int i = 0; i < 4; ++i) {
                    src[i] = &source_data[(next_x_row + i) * source_byte_row_stride];
                    out_row[i] = row_buffer.AdvanceRow();
                }
                simd.convolve_4rows_horizontally(src, filter_x, out_row);
                next_x_row += 4;
            } else {
                // Check if we need to avoid SSE2 for this row.
                if (simd.convolve_horizontally && next_x_row < last_filter_offset + last_filter_length - avoid_simd_rows) {
                    simd.convolve_horizontally(
                        &source_data[next_x_row * source_byte_row_stride],
                        filter_x, row_buffer.AdvanceRow(), source_has_alpha);
                } else {
                    if (source_has_alpha) {
                        ConvolveHorizontally<true>(
                            &source_data[next_x_row * source_byte_row_stride],
                            filter_x, row_buffer.AdvanceRow());
                    } else {
                        ConvolveHorizontally<false>(
                            &source_data[next_x_row * source_byte_row_stride],
                            filter_x, row_buffer.AdvanceRow());
                    }
                }
                next_x_row++;
            }
        }

        // Compute where in the output image this row of final data will go.
        unsigned char* cur_output_row = &output[out_y * output_byte_row_stride];

        // Get the list of rows that the circular buffer has, in order.
        int first_row_in_circular_buffer;
        unsigned char* const* rows_to_convolve = row_buffer.GetRowAddresses(&first_row_in_circular_buffer);

        // Now compute the start of the subset of those rows that the filter
        // needs.
        unsigned char* const* first_row_for_filter = &rows_to_convolve[filter_offset - first_row_in_circular_buffer];

        if (simd.convolve_vertically) {
            simd.convolve_vertically(filter_values, filter_length,
                first_row_for_filter,
                filter_x.num_values(), cur_output_row,
                source_has_alpha);
        } else {
            ConvolveVertically(filter_values, filter_length,
                first_row_for_filter,
                filter_x.num_values(), cur_output_row,
                source_has_alpha);
        }
    }
}

void SingleChannelConvolveX1D(const unsigned char* source_data,
    int source_byte_row_stride,
    int input_channel_index,
    int input_channel_count,
    const ConvolutionFilter1D& filter,
    const SkISize& image_size,
    unsigned char* output,
    int output_byte_row_stride,
    int output_channel_index,
    int output_channel_count,
    bool absolute_values)
{
    int filter_offset, filter_length, filter_size;
    // Very much unlike BGRAConvolve2D, here we expect to have the same filter
    // for all pixels.
    const ConvolutionFilter1D::Fixed* filter_values = filter.GetSingleFilter(&filter_size, &filter_offset, &filter_length);

    if (filter_values == NULL || image_size.width() < filter_size) {
        NOTREACHED();
        return;
    }

    int centrepoint = filter_length / 2;
    if (filter_size - filter_offset != 2 * filter_offset) {
        // This means the original filter was not symmetrical AND
        // got clipped from one side more than from the other.
        centrepoint = filter_size / 2 - filter_offset;
    }

    const unsigned char* source_data_row = source_data;
    unsigned char* output_row = output;

    for (int r = 0; r < image_size.height(); ++r) {
        unsigned char* target_byte = output_row + output_channel_index;
        // Process the lead part, padding image to the left with the first pixel.
        int c = 0;
        for (; c < centrepoint; ++c, target_byte += output_channel_count) {
            int accval = 0;
            int i = 0;
            int pixel_byte_index = input_channel_index;
            for (; i < centrepoint - c; ++i) // Padding part.
                accval += filter_values[i] * source_data_row[pixel_byte_index];

            for (; i < filter_length; ++i, pixel_byte_index += input_channel_count)
                accval += filter_values[i] * source_data_row[pixel_byte_index];

            *target_byte = BringBackTo8(accval, absolute_values);
        }

        // Now for the main event.
        for (; c < image_size.width() - centrepoint;
             ++c, target_byte += output_channel_count) {
            int accval = 0;
            int pixel_byte_index = (c - centrepoint) * input_channel_count + input_channel_index;

            for (int i = 0; i < filter_length;
                 ++i, pixel_byte_index += input_channel_count) {
                accval += filter_values[i] * source_data_row[pixel_byte_index];
            }

            *target_byte = BringBackTo8(accval, absolute_values);
        }

        for (; c < image_size.width(); ++c, target_byte += output_channel_count) {
            int accval = 0;
            int overlap_taps = image_size.width() - c + centrepoint;
            int pixel_byte_index = (c - centrepoint) * input_channel_count + input_channel_index;
            int i = 0;
            for (; i < overlap_taps - 1; ++i, pixel_byte_index += input_channel_count)
                accval += filter_values[i] * source_data_row[pixel_byte_index];

            for (; i < filter_length; ++i)
                accval += filter_values[i] * source_data_row[pixel_byte_index];

            *target_byte = BringBackTo8(accval, absolute_values);
        }

        source_data_row += source_byte_row_stride;
        output_row += output_byte_row_stride;
    }
}

void SingleChannelConvolveY1D(const unsigned char* source_data,
    int source_byte_row_stride,
    int input_channel_index,
    int input_channel_count,
    const ConvolutionFilter1D& filter,
    const SkISize& image_size,
    unsigned char* output,
    int output_byte_row_stride,
    int output_channel_index,
    int output_channel_count,
    bool absolute_values)
{
    int filter_offset, filter_length, filter_size;
    // Very much unlike BGRAConvolve2D, here we expect to have the same filter
    // for all pixels.
    const ConvolutionFilter1D::Fixed* filter_values = filter.GetSingleFilter(&filter_size, &filter_offset, &filter_length);

    if (filter_values == NULL || image_size.height() < filter_size) {
        NOTREACHED();
        return;
    }

    int centrepoint = filter_length / 2;
    if (filter_size - filter_offset != 2 * filter_offset) {
        // This means the original filter was not symmetrical AND
        // got clipped from one side more than from the other.
        centrepoint = filter_size / 2 - filter_offset;
    }

    for (int c = 0; c < image_size.width(); ++c) {
        unsigned char* target_byte = output + c * output_channel_count + output_channel_index;
        int r = 0;

        for (; r < centrepoint; ++r, target_byte += output_byte_row_stride) {
            int accval = 0;
            int i = 0;
            int pixel_byte_index = c * input_channel_count + input_channel_index;

            for (; i < centrepoint - r; ++i) // Padding part.
                accval += filter_values[i] * source_data[pixel_byte_index];

            for (; i < filter_length; ++i, pixel_byte_index += source_byte_row_stride)
                accval += filter_values[i] * source_data[pixel_byte_index];

            *target_byte = BringBackTo8(accval, absolute_values);
        }

        for (; r < image_size.height() - centrepoint;
             ++r, target_byte += output_byte_row_stride) {
            int accval = 0;
            int pixel_byte_index = (r - centrepoint) * source_byte_row_stride + c * input_channel_count + input_channel_index;
            for (int i = 0; i < filter_length;
                 ++i, pixel_byte_index += source_byte_row_stride) {
                accval += filter_values[i] * source_data[pixel_byte_index];
            }

            *target_byte = BringBackTo8(accval, absolute_values);
        }

        for (; r < image_size.height();
             ++r, target_byte += output_byte_row_stride) {
            int accval = 0;
            int overlap_taps = image_size.height() - r + centrepoint;
            int pixel_byte_index = (r - centrepoint) * source_byte_row_stride + c * input_channel_count + input_channel_index;
            int i = 0;
            for (; i < overlap_taps - 1;
                 ++i, pixel_byte_index += source_byte_row_stride) {
                accval += filter_values[i] * source_data[pixel_byte_index];
            }

            for (; i < filter_length; ++i)
                accval += filter_values[i] * source_data[pixel_byte_index];

            *target_byte = BringBackTo8(accval, absolute_values);
        }
    }
}

void SetUpGaussianConvolutionKernel(ConvolutionFilter1D* filter,
    float kernel_sigma,
    bool derivative)
{
    DCHECK(filter != NULL);
    DCHECK_GT(kernel_sigma, 0.0);
    const int tail_length = static_cast<int>(4.0f * kernel_sigma + 0.5f);
    const int kernel_size = tail_length * 2 + 1;
    const float sigmasq = kernel_sigma * kernel_sigma;
    std::vector<float> kernel_weights(kernel_size, 0.0);
    float kernel_sum = 1.0f;

    kernel_weights[tail_length] = 1.0f;

    for (int ii = 1; ii <= tail_length; ++ii) {
        float v = std::exp(-0.5f * ii * ii / sigmasq);
        kernel_weights[tail_length + ii] = v;
        kernel_weights[tail_length - ii] = v;
        kernel_sum += 2.0f * v;
    }

    for (int i = 0; i < kernel_size; ++i)
        kernel_weights[i] /= kernel_sum;

    if (derivative) {
        kernel_weights[tail_length] = 0.0;
        for (int ii = 1; ii <= tail_length; ++ii) {
            float v = sigmasq * kernel_weights[tail_length + ii] / ii;
            kernel_weights[tail_length + ii] = v;
            kernel_weights[tail_length - ii] = -v;
        }
    }

    filter->AddFilter(0, &kernel_weights[0], kernel_weights.size());
}

} // namespace skia
